Package: theftdlc 0.1.2

Trent Henderson

theftdlc: Analyse and Interpret Time Series Features

Provides a suite of functions for analysing, interpreting, and visualising time-series features calculated from different feature sets from the 'theft' package. Implements statistical learning methodologies described in Henderson, T., Bryant, A., and Fulcher, B. (2023) <arxiv:2303.17809>.

Authors:Trent Henderson [cre, aut]

theftdlc_0.1.2.tar.gz
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theftdlc.pdf |theftdlc.html✨
theftdlc/json (API)

# Install 'theftdlc' in R:
install.packages('theftdlc', repos = c('https://hendersontrent.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/hendersontrent/theftdlc/issues

On CRAN:

data-sciencedata-visualizationmachine-learningstatisticstime-series

4.92 score 3 stars 11 scripts 271 downloads 9 exports 107 dependencies

Last updated 2 months agofrom:1c4b8fc5c0. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 03 2024
R-4.5-winOKNov 03 2024
R-4.5-linuxOKNov 03 2024
R-4.4-winOKNov 03 2024
R-4.4-macOKNov 03 2024
R-4.3-winOKNov 03 2024
R-4.3-macOKNov 03 2024

Exports:calculate_intervalclassifyclustercompare_featuresfilter_duplicatesintervalprojectreduce_dimstsfeature_classifier

Dependencies:anytimeaskpassbackportsBHbroomclassclicodetoolscolorspacecorrectRcpp11curldigestdistributionaldplyre1071ellipsisfabletoolsfansifarverfeastsforecastfracdifffurrrfuturegenericsggdistggplot2globalsgluegtableherehmsisobandjanitorjsonlitelabelinglatticelifecyclelistenvlmtestlubridatemagrittrMASSMatrixmclustmgcvmunsellnlmennetnormaliseRnumDerivopensslparallellypillarpkgconfigplyrpngprogressrproxypurrrquadprogquantmodR.matlabR.methodsS3R.ooR.utilsR6rappdirsRcatch22RColorBrewerRcppRcppArmadilloRcppEigenRcppRollRcppTOMLreshape2reticulaterlangrprojrootRSpectraRtsnescalesslidersnakecasestringistringrsysthefttibbletidyrtidyselecttimechangetimeDatetseriestsfeaturestsibbleTTRumapurcautf8vctrsviridisLitewarpwithrxtszoo

Introduction to theftdlc

Trent Henderson

Rendered fromtheftdlc.Rmdusingknitr::rmarkdownon Nov 03 2024.

Last update: 2024-03-19
Started: 2024-02-25

Readme and manuals

Help Manual

Help pageTopics
Fit classifiers using time-series features using a resample-based approach and get a fast understanding of performanceclassify tsfeature_classifier
Perform cluster analysis of time series using their feature vectorscluster
Conduct statistical testing on time-series feature classification performance to identify top features or compare entire setscompare_features
Remove duplicate features that exist in multiple feature sets and retain a reproducible random selection of one of themfilter_duplicates
Filter resample data sets according to good feature listfilter_good_features
Helper function to find features in both train and test set that are "good"find_good_features
Fit classification model and compute key metricsfit_models
Calculate central tendency and spread values for all numeric columns in a datasetget_rescale_vals
Calculate interval summaries with a measure of central tendency of classification resultscalculate_interval interval
Helper function for converting to title casemake_title
Produce a plot for a feature_calculations objectplot.feature_calculations
Produce a plot for a feature_projection objectplot.feature_projection
Project a feature matrix into a two-dimensional representation using PCA, MDS, t-SNE, or UMAP ready for plottingproject reduce_dims
Helper function to create a resampled datasetresample_data
Calculate z-score for all columns in a dataset using train set central tendency and spreadrescale_zscore
Helper function to select only the relevant columns for statistical testingselect_stat_cols
Calculate p-values for feature sets or features relative to an empirical null or each other using resampled t-testsstat_test
Analyse and Interpret Time Series Featurestheftdlc-package theftdlc